Role Overview

You will lead the design, analysis, and interpretation of population genomics studies using large-scale datasets (WES/WGS/eQTL/pQTL), contributing directly to disease research and therapeutic development.

Key Responsibilities
Design and execute population genomics studies for rare and complex disease research leveraging multi-modal integrative analyses
Well versed with NGS data analyses- WGS/ WES data, variant annotation and interpretation, QTL analyses (eQTL, pQTL, mQTL, sQTL)
Genetic association analyses (GWAS) and downstream analyses (fine-mapping, LD mapping, causal gene-phenotype correlation, Mendelian randomization)
Solid statistical background- regression, glm, MDR, PCA prediction modelling, Bayesian statistics, SVM
Prior experience in tools like PLINK, GCTA, ANNOVAR, samtools, bcftools, GATK
Build and apply pipelines for:
Variant calling and quality control
Genotype imputation and fst statistics




Haplotyping and linkage disequilibrium analysis
Variant annotation and gene-aggregate testing (SKAT, burden tests, etc.)
Polygenic risk score (PRS) modelling- PRSice, LDPred2
Utilize and interpret data from genomic and transcriptomic resources such as gnomAD, ClinVar, GWAS Catalog, and PRS Catalog

Required Qualifications
Ph.D. in Genomics, Statistical Genetics, Bioinformatics, or a related discipline (or equivalent industry experience)
Solid understanding of population genetics, statistical modeling, and genomic data interpretation

Preferred Qualifications
Programming experience in Python and/or R
Experience working in HPC environments or cloud platforms (AWS, GCP, Azure)
Prior exposure to complex and rare disease genomics or building optimised pipelines for translational research workflows

📌 Scientist Statistical Genomics Hyderabad (India)
🏢 Aganitha Cognitive Solutions
📍 India